• Corpus ID: 237940091

onlineforecast: An R package for adaptive and recursive forecasting

@inproceedings{Bacher2021onlineforecastAR,
  title={onlineforecast: An R package for adaptive and recursive forecasting},
  author={Peder Bacher and Hj{\"o}rleifur G. Bergsteinsson and Linde Frolke and Mikkel L. S{\o}rensen and Julian Lemos-Vinasco and Jon A. R. Liisberg and Jan Kloppenborg M{\o}ller and Henrik Aalborg Nielsen and Henrik Madsen},
  year={2021}
}
Systems that rely on forecasts to make decisions, e.g. control or energy trading systems, re-quire frequent updates of the forecasts. Usually, the forecasts are updated whenever new observations become available, hence in an online setting. We present the R package onlineforecast that provides a generalized setup of data and models for online forecasting. It has functionality for time-adaptive fitting of dynamical and non-linear models. The setup is tailored to enable the effective use of… 

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References

SHOWING 1-10 OF 42 REFERENCES

Automatic Time Series Forecasting: The forecast Package for R

Two automatic forecasting algorithms that have been implemented in the forecast package for R, based on innovations state space models that underly exponential smoothing methods, are described.

ForecastTB—An R Package as a Test-Bench for Time Series Forecasting—Application of Wind Speed and Solar Radiation Modeling

Real application examples with natural time series datasets are presented to exhibit the features of the ForecastTB package to evaluate forecasting comparison analysis as affected by the characteristics of a dataset.

Online short-term solar power forecasting

Using quantile regression to extend an existing wind power forecasting system with probabilistic forecasts

An existing wind power forecasting system (Zephyr/WPPT) is considered and it is shown how analysis of the forecast error can be used to build a model of the quantiles of the Forecast Error, whereby the model obtained can beused for providing situation-dependent information regarding the uncertainty.

PREDICTION OF WIND POWER USING TIME-VARYING COEFFICIENT-FUNCTIONS

A method for adaptive and recursive estimation in a class of non-linear autore- gressive models with external input is proposed. The model class considered is conditionally parametric ARX-models

Forecasting with Exponential Smoothing: The State Space Approach

I. Introduction: Basic concepts.- Getting started. II. Essentials: Linear innovations state space models.- Non-linear and heteroscedastic innovations state space models.- Estimation of innovations

Short-term heat load forecasting for single family houses

Out-of-sample tests of forecasting accuracy: an analysis and review